I got my BS from Dept. of Electronic Engineering, Tsinghua University, Beijing, China in 2004. From 2004 to 2005, I was in Microsoft Research Asia, as a visiting student in the Visual Computing Group. I got my MS in Dept. of EE from University of California, Riverside, 2008.

Research Interests:

Computer Vision andStatistical Machine Learning

My current research focuses on image analysis and enhancement, especially by using Tensor Voting method to explore the image geometric structure.

Besides, I am quite interesting in the link between practical algorithms and the theory, e.g., Riemannian Manifold, Probability measure on geometric objects lie in different manifolds

I am also interesting on the how to apply sparse representation, sparse learning and compressive sensing into computer vision research, for both low-level and high-level tasksd.

Though there are many image decomposition methods, it is hard to get both of the basis and the features to be independent without the normal distribution assumption. Recent research shows that sparseness and other constraints will lead to part-based representations results, which is similar to the receptive fields in V1 cortex in human Brain. Sparse Coding, Sparse Bayesian Learning and Compressive Sensing have been proposed for pattern learning, feature extraction, denoising and compression during the past 10 years.

In vision, self-taught learning means studying the knowledge from free-cost images in our natural environment; it is an active area in machine learning in recent years. The significance of self-taught learning is to revisit the fact that sometimes not only the labeled target data but also the relevant unlabeled data are hard to get, while at the same time the basic patterns can be embedded in the general data although it is unlabeled and with quite different distribution.

Propose a model-based feature extraction approach, which uses micro-structure modeling to design adaptive micro-patterns. We first model the micro-structure of the image by Pair-wise Markov Random Field. Then we give the generalized definition of micro-pattern based on the model. After that, we define the fitness function and compute the fitness index to encode the image’s local fitness to micro-patterns.

Papers: ICIP08, ICCV05(AMFG), US Patents

Riemannian Manifold Learning and Graph Spectral Analysis:

The relationship between manifold dimension reduction and sparseness representations; theoretical understanding of manifold in the information theory framework.

During the spare time, I love watching movies and listening pop music. Especially, I was a director in TDO (TDO means trade-off, it is extremely important for team work^_^) studio, Tsinghua University. We made two student movies and one music video. One of the movie is "how do I love you", which got the best idea and best actor prizes in the first Tsinghua Digital Movie Festival, 2004.

Here is the link of this movie in youku. Actually, I just found it out by accident and I really do not know who put this online, but it is fine, enjoy it:-).